Relationship between Stock Index Returns and Inflation-Megha -0488

A Research Project on

An Empirical Analysis of Relationship between Stock Index

Returns and Inflation.

Submitted in partial fulfillment of the requirement of the MBA degree

Bangalore University

Submitted by MEGHA.N.BAIS

Register number

04XQCM6054

Under the guidance of

Dr. Nagesh Malavalli

M.P.Birla Institute of Management,

Bangalore.

M.P.Birla Institute of Management,

Associate Bharatiya Vidya Bhavan,

Bangalore 560001

DECLARATION

I hereby declare that the research work embodied in the dissertation

entitled “An Empirical Analysis of Relationship between Stock Index Returns and

Inflation”” is the result of research work carried out by me, under the

guidance and supervision of Dr. Nagesh Malavalli, Principal, M. P. Birla

Institute of Management, Associate Bharatiya Vidya Bhavan, Bangalore.

I also declare that the dissertation has not been submitted to any

University/Institution for the award of any Degree/Diploma.

Place: Bangalore MEGHA N.BAIS Date: Regd. No. 04XQCM6054.

GUIDE’S CERTIFICATE

This is to certify that the Project titled “An empirical Analysis of

Relationship between stock Index Returns and Inflation” has been prepared by Ms.

MEGHA N.BAIS bearing registration number 04XQCM6054, under the

guidance of Dr. Nagesh Malavalli, M.P.Birla Institute of Management,

Bangalore. This has not formed a basis for the award of any degree/diploma

for any university.

Place: Bangalore Date: Dr. Nagesh Malavalli

PRINCIPAL’S CERTIFICATE

This is to certify that the Project titled “An Empirical Analysis of

Relationship between Stock Index Return and Inflation” has been prepared by Ms.

MEGHA N.BAIS bearing registration number 04XQCM6054, under the

guidance of Dr. Nagesh Malavalli, M.P.Birla Institute of Management,

Associate Bharatiya Vidya Bhavan, Bangalore.

Place: Bangalore Date: Dr. Nagesh Malavalli

ACKNOWLEDGEMENT

“Blessed are those who give without remembering and

Blessed are those who take without forgetting”.

The completion of the research would have been impossible without the valuable

contributions of people from the academics, family and friends.

I hereby wish to express my sincere gratitude to all those who supported me

throughout the study.

I am thankful to Dr. NAGESH MALAVALLI, Principal, M.P.Birla Institute of

Management, Bangalore, for his valuable guidance, academic and moral support which

made this report a reality.

I am greatly thankful to Prof. T.V. Narasimha Rao (Finance), Prof. Santhanam

(Statistics) Prof. Bislaiah (Economics) for their support in completion of this report.

A special thanks to my friend Lakshmi S.N. who made this report reality.

Last but certainly not the least, my family and friends who tolerated me and

cooperated when I was not so very best.

Place: Bangalore MEGHA N.BAIS

Date: Reg No: 04XQCM6054

ABSTRACT

Stock market and Inflation are the barometers of the economy and both are the

sensitive segments of the economy. Any changes in the policies of the country are

quickly reflected in the stock market and inflation. There are different factors, which

affect the stock markets like interest rates, company performance, future growth

prospects, political stability, exchange rates etc. There are different factors, which affect

the Inflation like the money supply, interest rates, faith in government's ability to protect

the value of currency, monetary and fiscal policy, budgetary deficits etc. This study

attempts to analyze the interlinkages between inflation and stock prices.

The study is conducted by considering inflation and various indices for various

periods. This is analyzed by using statistical tools like Augmented Dickey Fuller Unit

root Test, Grangers Co-integration test and Grangers causality test.

From the results it is clear that there is negative relationship between stock returns

and inflation, and suggests that investors cannot use common stock investments as a

hedge against rising prices or inflation. It is evident from the overall results that the

causality runs from inflation to stock returns and also in the reverse order.

CONTENTS

Chapter No.

Particulars Page No.

Abstract

1 Introduction - Background - Theoretical Framework

1-13

2 Literature Review

14 -18

3 Research Methodology - Problem Statement - Objectives of the study - Purpose of the study - Hypothesis - Sample Design - statistical Methods - Limitations of the study

19 – 31

4 Empirical Results

32 – 48

5 Conclusions

49 – 51

Bibliography

52 – 53

Annexure

54 - 67

LIST OF TABLES

Table No.


1 List of S&P CNX Nifty Companies

22

2 List of CNX Bank Index Companies

24

3 List of BSE Sensex Companies

24

4 Results of Wholesale Price Index ADF Unit Root Test

32

5 Results of S&P CNX Nifty ADF Unit Root Test

34

6 Results of CNX Bank Index ADF Unit Root Test

36

7 Results of BSE sensex ADF Unit Root Test

38

8 Results of S&P CNX Nifty and WPI Regression

40

9 Results of S&P CNX Nifty and WPI Granger Co-integration Test

40

10 Results of CNX Bank Index and WPI Regression

42

11 Results of CNX Bank Index and WPI Granger Co-integration Test

42

12 Results of BSE Sensex and WPI Regression

44

13 Results of BSE sensex and WPI Granger Co-integration Test

44

14 Results of S&P CNX Nifty and WPI Grangers Causality Test

46

15 Results of CNX Bank Index and WPI Grangers Causality Test

47

16 Results of BSE Sensex and WPI Grangers Causality Test

48

LIST OF GRAPHS

Graph No.


1 S&P CNX Nifty Stationarity Graph

33

2 CNX Bank Index Stationarity Graph

35

3 BSE Sensex Stationarity Graph

37

Introduction Background

Globalization and financial sector reforms in India have ushered in a sea change

in the financial architecture of the economy. In the contemporary scenario, the activities

in the financial markets and their relationships with the real sector have assumed

significant importance. Since the inception of the financial sector reforms in the

beginning of 1990’s, the implementation of various reform measures including a number

of structural and institutional changes in the different segments of the financial markets,

particularly since 1997, have brought about a dramatic change in the financial

architecture of the economy. Altogether, the whole gamut of institutional reforms,

introduction of new instruments, change in procedures, widening of network of

participants call for a reexamination of the relationship between the financial sector and

the real sector in India. Correspondingly, researches are also being conducted to

understand the current working of the economic and the financial system in the new

scenario. Interesting results are emerging particularly for the developing countries where

the markets are experiencing new relationships which are not perceived earlier. The

analysis on stock markets has come to the fore since this is the most sensitive segment of

the economy. It analyses the relationship between stock prices and macroeconomic

variable inflation with implications on efficiency of stock prices.

Relationship between stock returns and inflation

There are several empirical explanations for the negative correlation between

stock returns & inflation. Some attribute this inconsistency between the theory &

empirical findings to market inefficiency. Others attribute it to the negative correlation

between real economic activity (fiscal & monetary) and inflation known as proxy effect

hypothesis. In an increasingly complex scenario of the financial world, it is of paramount

importance for the researchers, practitioners, market players and policy makers to

understand the working of the economic and financial system and assimilate the mutual

interlink ages between the stock and economic variables in forming their expectations

about the future policy and financial variables

The informational efficiency of major stock markets has been extensively

examined through the study of causal relations between stock price indices and inflation.

The findings of these studies are important since informational inefficiency in stock

market implies on the one hand, that market participants are able to develop profitable

trading rules and thereby can consistently earn more than average market returns, and on

the other hand, that the stock market is not likely to play an effective role in channeling

financial resources to the most productive sectors of the economy.

The Efficient Markets Hypothesis (EMH) assumes that everyone has perfect

knowledge of all information available in the market. Therefore, the current price of an

individual stock (and the market as a whole) portrays all information available at time t.

accordingly, if real economic activity affects stock prices, then an efficient stock market

instantaneously digests and incorporates all available information about economic

variables. The rational behavior of market participants ensures that past and current

information is fully reflected in current stock prices. As such, investors are not able to

develop trading rules and, thus may not consistently earn higher than normal returns.

Therefore, it can be concluded that, in an information ally efficient market, past (current)

levels of economic activity are not useful in predicting current (future) stock prices.

While finding causality from lagged values of stock prices to an economic

aggregate does not violate informational efficiency, this finding is equivalent to the

existence of causality from current values of stock prices to future levels of the economic

variable. This would suggest that stock prices lead the economic variable and that the

stock market makes rational forecasts of the real sector.

If, however, lagged changes in some economic variables cause variations in stock

prices and past fluctuations in stock prices cause variations in the economic variable, then

bi-directional causality is implied between the two series. This behavior indicates stock

market inefficiency. In contrast, if changes in the economic variable neither influence nor

are influenced by stock price fluctuations, then the two series are independent of each

other and the market is information ally efficient.

The inflation rate is an important element in determining stock returns due to the

fact that during the times of high inflation, people recognize that the market is in a state

of economic difficulty. People are laid off work, which could cause production to

decrease. When people are laid off, they tend to buy only the essential items. Thus

production is cut even further. This eats into corporate profits, which in turn makes

dividends diminish. When dividends decrease, the expected return of stocks decrease,

causing stocks to depreciate in value.

Inflation clearly affects different companies in different ways. Inflation is

essentially a disequilibrium phenomenon involving continuing distortions among relative

prices. These distortions result partly from time lags. A given impulse of money and

credit inflation pushes up different prices and wages at different rates and by varying

magnitudes. Moreover, when countries inflate at different rates, relative exchange rates

are distorted, and this in turn feeds back into the domestic price structure, altering relative

prices even further. There are also institutional factors which affect relative prices, for

example import controls, unions, and regulation and deregulation of such things as oil

and transport. Thus relative prices will shift during a period of fluctuating inflation rates,

affecting the growth and stability of earnings.

Common stock represents an ownership claim on the prospective after-tax

earnings of the company. Thus, an unexpected increase in the inflation rate would tend to

depress the after tax earnings on capital, thereby depressing the value of corporate assets

to potential owners. Accordingly, real stock prices tend to fall.

It has only been in periods of accelerating inflation and tight monetary policy that

the market has really been poor. The depressing effect of accelerating inflation on the

stock market resulted from the perceived risk by investors that the monetary authorities

would tighten policy in order to control inflation, and that this would work by depressing

the economy and the real earnings of the corporate sector.

The overall level of the stock market will be affected by cyclical movements of

the economy. Prices will rise at times of easy money and low interest rates, which

provide a stimulus to economic growth. As interest rates and money tightens, so the

business environment will worsen, costs will rise, demand will fall, profits will be

squeezed from both sides, and stock prices will become depressed.

Theoretical Framework The Indian Financial System

The Indian financial system consists of many institutions, instruments and

markets. Financial instruments range from the common coins, currency notes and

cheques, to the more exotic futures swaps of high finance.

Financial Markets

Generally speaking, there is no specific place or location to indicate financial

markets. Wherever a financial transaction takes place, it is deemed to have taken place in

the financial market. Hence financial markets are pervasive in nature since financial

transactions are themselves very pervasive throughout the economic system.

However, financial markets can be referred to as those centers and arrangements

which facilitate buying and selling of financial assets, claims and services. Sometimes,

we do find the existence of a specific place or location for a financial market as in the

case of stock exchange.

Classification of financial markets

Financial markets can be classified as

i) Unorganized Markets

In these markets there a number of money lenders, indigenous bankers, traders

etc. who lend money to the public.

ii) Organized Market

In organized markets, there are standardized rules and regulations governing their

financial dealings. There is also a high degree of institutionalization and

instrumentalization. These markets are subject to strict supervision and control by the

RBI or other regulatory bodies.

Organized markets can be further divided into capital market and Money market.

Capital market

Capital market is a market for financial assets which have a long or definite

maturity. Which can be further divided into:

• Industrial Securities Market

• Government Securities Market

• Long Term Loans Market

Industrial Securities Market

It is a market where industrial concerns raise their capital or debt by issuing

appropriate Instruments. It can be subdivided into two. They are:

• Primary Market or New Issues Market

Primary market is a market for new issues or new financial claims. Hence, it is

also called as New Issues Market. The primary market deals with those securities which

are issued to the public for the first time.

• Secondary Market or Stock Exchange

Secondary market is a market for secondary sale of securities. In other words,

securities which have already passed through the new issues market are traded in this

market. Such securities are listed in stock exchange and it provides a continuous and

regular market for buying and selling of securities. This market consists of all stock

exchanges recognized by the government of India.

Importance of Capital Market

Absence of capital market serves as a deterrent factor to capital formation and

economic growth. Resources would remain idle if finances are not funneled through

capital market.

• It serves as an important source for the productive use of the economy’s savings.

• It provides incentives to saving and facilitates capital formation by offering

suitable rates of interest as the price of the capital

• It provides avenue for investors to invest in financial assets.

• It facilitates increase in production and productivity in the economy and thus

enhances the economic welfare of the society.

• A healthy market consisting of expert intermediaries promotes stability in the

value of securities representing capital funds.

• It serves as an important source for technological up gradation in the industrial

sector by utilizing the funds invested by the public.

Inflation

Definition

Inflation is an increase in the amount of currency in circulation, resulting in a

relatively sharp and sudden fall in its value and rise in prices: it may be caused by an

increase in the volume of paper money issued or of gold mined, or a relative increase in

expenditures as when the supply of goods fails to meet the demand.

This definition includes some of the basic economics of inflation and would seem

to indicate that inflation is not defined as the increase in prices but as the increase in the

supply of money that causes the increase in prices i.e. inflation is a cause rather than an

effect.

On the other hand in this definition, inflation would appear to be the consequence

or result (rising prices) rather than the cause -- A persistent increase in the level of

consumer prices or a persistent decline in the purchasing power of money, caused by an

increase in available currency and credit beyond the proportion of available goods and

services.

High and Low inflation

It would seem obvious that low inflation is good for consumers, because costs are

not rising faster than their paychecks.

During the high inflation it is believed that "High Inflation introduces

uncertainty". This is not quite true either. The truth is that steady inflation, if it can be

relied upon to remain steady, does not introduce uncertainty. Changing (fluctuating)

inflation rates is what introduces uncertainty. The best stock market performance takes

place in years of price stability when nothing is happening on the inflation & deflation

front.

Causes

There may be difference of opinion on the causes and consequences of inflation

and the measures to be taken to deal with the problem. What needs to be done is not so

much a general statement of anti inflationary policies as the formulation, in great detail,

of remedial measures, short term as well as long term.

It is the result of economic forces at work, rather than the conspiracy of merchants

and manufacturers, or the faulty functioning of the market mechanism, these can only be

short period.

Inflation represents an imbalance between the flow of incomes to people and the

spending power available with them on the one hand and the availability of goods and

services on the other.

Inflation can occur with unchanged availability of goods and a marked increase in

incomes in the hands of the people and desire to spend from past savings. There are also

situations were production remains sluggish and even declines while incomes in the

hands of the public rises on account of high levels of government and non government,

financed by borrowing from the banking system in a big way.

If inflation continues for a long period, it causes a lot of disturbance to the

economy which gets distorted. The price rise in the country is on account of factors

operating both on the demand & supply sides:

• Expenditure by government larger than its receipts from revenue and loans from the

public, which is known as deficit financing.

Where the deficit financing is of large dimensions, naturally the flow of

incomes is proceeding at a much faster pace than the capacity of the economy to

generate a corresponding larger supply of goods and services.

• Larger credit given by banks to the commercial sector not supported by productive

activities.

The RBI regulates the latter by imposing restrictions on credit in order to

bring about some equilibrium. The former can be controlled by government

imposing greater self discipline and keeping its expenditure within the limits of its

resources.

• Demand generated by unaccounted money.

• Delay in monsoon

• Increase in the purchasing power of the house holds.

• Liberal govt. policies on taxation, excise, customs etc.

• Expansion of currency.

Inflation can be avoided

Price stability can be accomplished, provided there is a will on the part of the

govt. and public in this direction. The fight against inflation calls for a proper formulation

of economic plans and determined implementation of the plans. Fiscal & monetary action

constitutes important elements of the anti inflationary strategy. Various types of action

should be taken to raise the standards of productivity in the farm, factory & the office.

The immediate action should be to arrest forthwith any further rise in commodity prices,

by drastic action on the fiscal & monetary fronts. Measures may be:

• Role of fiscal policy

Reduction of budgetary deficits: Control over expenditure & maximizing tax

income and rise in the rates of interest rates. Ceiling should be put on purchase of

govt securities by the RBI and the commercial banks & the extension of ways &

advances to govt by the Reserve Bank.

• Role of monetary policy

The primary task of monetary policy is to restrain money circulation in the

economy. The RBI has a variety of instruments of credit control. It si the

responsibility of individual commercial banks to ensure that credit allocation to

the various sectors of the economy is done in a manner that fulfills broadly the

official objective of larger credit flows to the neglected & priority sectors while at

the same time keeping down the aggregate extension of credit to the limits

dictated by the overall economic situation. This can be done through selective

credit control; cash reserve ratio, statutory ratios & open market operations.

• Role of the corporate sector

Corporate sector can curb inflation by substantial increase in productivity,

decreasing inventory building.

Types of Inflation

Open inflation: when prices rise substantially and continuously, the phenomenon is

called open inflation.

Latent inflation: for a relatively short period there may be no increase in pieces but there

will be a substantial buildup of what is known as latent inflation. The

community is building up its liquid resources – cash, bank deposits

and short term investments.

Demand pull: it arises when prices are rising as a result of growing demand for goods

and services in relation to their supplies. Generally, it is caused by rising

current & capital expenditure on the part of the govt and the public.

Y0 is the level of real national income. An aggregate demand function D0

intersecting aggregate supply function S at point A. the price level remains at P0. an

upward shift in aggregate demand function will simply raise the price level. It can be seen

in the diagram that a rise in aggregate demand indicated by the aggregate demand

function D1 merely raises the price level to P1 without any impact on real national

income. This is a clear case of what is known as excess demand inflation.

Cost push inflation: it arises when there is a substantial increase in cost, on account of

wage & salary increase much in excess of productivity increases &

increase in the prices of important goods & services.

In an imperfectly competitive economy the aggregate supply function cannot be

assumed to be stable. Consequently, the aggregate supply curve move upward as from S0

to S1 & S2 regardless of the behavior of aggregate demand. The intersection of original

aggregate supply curve S0 & the aggregate demand curve D0 had ensured real income at

point level P0. When the aggregate supply function moves upward from S0 to S1 due to

rising costs, level of real income can be maintained only if the price level is raised to

OP1. An effort to hold prices closer to p0 will result in a decline in employment &

corresponding fall in the level of income. This is a case of what economists call cost push

inflation.

Measures of Inflation

Two most widely used price indices are ‘consumer price index ‘and ‘wholesale

price index’.

Consumer price index:

It is the annual percentage change in the cost of acquiring a fixed basket of goods

and services. There are four different types of consumer price index released for different

levels of working class in the country viz. consumer price index for urban non manual

employee, consumer price inflation for agriculture laborers, consumer price index for

industrial workers, and consumer price inflation for rural laborers. Different

governmental and monitoring agencies use these indices for their purpose. These indices

also form the basis of decisions regarding the dearness allowance for the government

employees.

Wholesale price index:

This is the index that is used to measure the change in the average price level of

goods traded in wholesale market. A total of 435 commodities data on price level is

tracked. The Wholesale Price Index (WPI) is the most widely used price index in India. It

is the only general index capturing price movements in a comprehensive way. It is an

indicator of movement in prices of commodities in all trade and transactions. It is also the

price index which is available on a weekly basis with the shortest possible time lag only

two weeks. It is due to these attributes that it is widely used in business and industry

circles and in Government, and is generally taken as an indicator of the rate of inflation in

the economy. It is imperative that the index is put on as sound a footing as possible.

Calculation of inflation rate:

Rate for Inflation for year t = PIN t – PIN t-1

* 100

PIN t-1Where, PIN t is index for year t

PIN t-1 is index for previous year.

Literature Review A significant amount of literature exists that examines the relationship between

stock market returns and a range of macro economic and financial variables over a

number of different stock markets and time periods. Now a day financial economics

provide a number of models that helps to examine the relationship. Some of the

literatures on this topic are:

Fama and Schwert *1 (1977) in their study estimated the extent to which common

stocks are hedge against expected and unexpected components of inflation rate during

1953-71 periods. They found that the common stock returns are negatively related to the

expected components of the inflation and also to the unexpected components.

Their objectives was to find out the relationship between stock return and

expected inflation, whether stocks can be used as hedge against inflation and whether the

market is efficient in impounding available information about future inflation into stock

prices.

The data taken for their study was inflation rates from Jan.1953 to July 1971. The

common stocks taken are the continuously compounded stock of NYSE. They estimated

the relationship using the first twelve autocorrelation of the inflation rate and the nominal

returns for the monthly, quarterly and semiannual data. The returns to the NYSE are

approximately -5.5, with standard error of about 2.0, which implies that common stocks

are not a hedge against the expected inflation rates.

Thus, they concluded that common stock returns are negatively related to the

expected inflation rate during the period 1953-71, it cannot be used as hedge against

inflation. Possibility for negative relationship between common stock returns & the

expected inflation rate could be that the market might be inefficient in impounding

available information about future inflation into stock prices.

*1 indicates reference article no.1 Fama and Schwert. – see bibliography

Jacob Boudoukh and Mathew Richardson*2 (1993) are behind the two main

empirical facts regarding the statistical relation between stock returns & inflation. The

first is the ex post nominal stock returns and inflation are negatively correlated. The

second empirical result documents a negative relation between ex ante nominal stock

returns & ex ante inflation.

The main objective of their study was to find out the long term relationship

between stock returns and inflation. The data for the study is two centuries, 1802-1990

stock returns, short term & long term bonds, & inflation in both United States & United

Kingdom. The ex ante long term inflation has been arrived at by using an instrumental

variables approach. The instruments used are past inflation rates & short and long term

interest rates that have theoretical support as measures of ex ante inflation. Using ex post

inflation as proxy for ex ante inflation rates.

Jacob and Richardson took the help of regression to estimation the relationship.

For ex post relation, they regressed one year stock returns on one year inflation & five

year stock returns on five year inflation:

R t+1 = α 1+ β1 π t+1 +έt

Σ R t+i = α 5 + β5 Σ π t+i +έt

Hypothesis β5 = β1 versus the alternative β5 > β1. The results showed that the

regression coefficient of five year stock returns on the contemporaneous five year

inflation rate is significantly positive, β5 = 0.52, with a standard error of 0.17. Therefore,

nominal stock returns & inflation tend to move together over the sample, thus supporting

the view that stocks provide some compensation for movements in inflation. On the other

hand, they found that the estimate of β1 = 0.07 close to zero, indicating that the stocks

seems to compensate for inflation in the long run.

*2 indicate reference article no.2 Jacob Boudoukh and Matthew Richardson- see bibliography.

For the ex ante relation various instruments have been used by them. The

instrumental variable estimation is generated from the following system of equations:

E [(Σ R t+I – α j – β j Σ π t+I) Zjt] = 0

Where, Rt denotes stock returns, πt denoted inflation rates & Zjt is a set of

instruments associated with particular horizon j.The first set of instruments includes the

one year interest rate & the long tern interest rates. To capture the movements in one year

& five year expected inflation, respectively. The second set of instruments includes the

past one year & five year inflation rates. The results support a positive relation between

stock returns & ex ante inflation. Thus Jacob and Richardson provide strong support for a

positive relation between nominal stock returns & inflation at long horizon.

Bulent Gultekin*3(1983) investigates the relation between common stock &

inflation in 26 countries for the postwar period. The study found that there is a consistent

lack of positive relation between stock returns and inflation in most of the countries.

The data taken are monthly compounded inflation rates for individual countries

from January 1947 to December 1979. Stock market returns are obtained from IFS & CIP

indices of about 26 countries. He estimated the first four autocorrelation for inflation

rates & stock market returns for both IFS & CIP. Monthly inflation rates in almost all

countries have positive autocorrelations. IFS stock returns also have positive

autocorrelation.

In order to investigate the relation between nominal stock returns & inflation, the

regression model is estimated by using three different estimates of the expected inflation

rates, contemporaneous inflation rates as proxies for expected inflation, decomposition of

inflation into expected & unexpected components by ARIMA models and short term

interest rates are used as predictors of inflation.

*3 indicate article no 3 Bulent Gultekin – see bibliography

The regression result says that all regression coefficients for the expected inflation

rate are negative. Thus, these results indicate a stronger negative relation between stock

returns & expected inflation.

Bharat kolluri *4 (2005) has made an attempt to identify the casual influence of

inflation on stock returns and a reverse causality from stock return to inflation. The

results indicate bidirectional causality between these two variables.Many of the previous

studies focused on the interpretation of the puzzling negative relationship ignoring the

basic issue of causality. This study corrects this deficiency by examining the basic issue

of causality between stock returns and inflation.

The main objectives of the study was to find out the relationship between stock

returns and inflation using Grangers Co-integration test, the direction of causality

between stock returns and inflation and to find out whether common stock are hedge

against inflation or that they compensate investors for rising prices.

The data used are the continuously compounded monthly returns covering the

period from 1960:1 to 2004:12, of the US. The data on nominal stock returns (RE),

inflation (INF), and Real Treasury bill rate (RTB) are obtained from Ibbotson Associates

(2004). the common stock return measure is based on the Standard and Poor’s (S&P)

composite index. Methodology used is the unit root test called Augmented Dickey Fuller

test. This test is done to find out the stationarity of the time series data. The second used

here is the Grangers co integartion test. To examine if the nominal stock returns and

expected inflation are co-integrated, that is, if they move together for a long period of

time. This is done regressing the two variables on each other.Residuals generated from

such a regression should be stationary. The third test used is the Grangers Causality test.

This is done to find out the direction of causality.

*4 indicate article no 4 Bharat Kolluri – see bibliography.

The overall results support bidirectional causality between stock returns and

expected inflation including the negative sign in both directions. And that the stock

returns and inflation are negatively related as the coefficients of the regression equation

are negative.

Basabi Bhattacharya & Jaydeep Mukherjee*5 (2005) investigates the nature of the

causal relationship between stock prices and inflation in India. And it has been found that

there exists two way causation between stock prices and rate of inflation.

The purpose of the study was to analyze the relationship between stock prices and

inflation with implications on efficiency of stock markets and to determine whether stock

returns are a leading indicator for future real economic activity.

The data for the study is the monthly stock prices of BSE. Methodology used is

the Unit root test such as ADF to determine the stationarity of the data, Grangers co-

integration test to determine the co-integration between stock returns and inflation and

Toda and Yamamoto version Grangers non causality test to test lead and lag variable.

And for the selection of the Hsiao’s optimum lag length was used which has given

optimum lag length to be 2.

The study concluded that there is a bidirectional causality between stock price and

the rate of inflation, thus implying that the market informational efficiency hypothesis

can be rejected for BSE sensitive index.

*5 indicate article no 5 Basabi Bhattacharya and Jaydeep Mukherjee – see bibliography

Research Methodology

Problem statement There are various studies, which have been done to examine the relationship

between inflation and stock returns by taking various statistical models and various

indices. This study explores the evidence of relationship between inflation rates and stock

returns and also lead lag relationship between the two. And to find out whether the

market is efficient in impounding available information about future inflation into stock

prices.

Objectives of the study

• To analyze the relationship between stock return and inflation rate

• To find out whether the relationship changes with the different indices

• To find out which variable is leading and which variable is lagging.

• To find out whether common stock are hedge against inflation.

Purpose of the Study

The existence of a significant positive one to one relationship between the

nominal rate of interest and the rate of inflation, known as the Fisher effect has been very

well established and accepted for a long time in the economic literature. Fisher asserted

that the nominal interest rate consists of a real rate plus expected inflation rate.

1 + Nominal Rate = (1 + Real Rate) (1 + Expected Inflation Rate)

1 + r = (1 + a) (1 + i )

r = a + i +ai

r = a + i

Fisher hypothesis states that real rates of return on common stock and expected

inflation rates are independent and that nominal stock returns vary in one to one

correspondence with expected inflation. He believed that the real and monetary sectors

of the economy are largely unrelated. According to him expected real rate is determined

by real factors such as the productivity of capital and time preference of savers and is

independent of the expected inflation rate.

The expected nominal returns contain market assessments of expected inflation

rates. Thus, if the market is an efficient or rational processor of the information available

at time t-1, it will set the price of an asset j so that the expected nominal return on the

asset from t-1 to t is the sum of the expected real return and the best possible assessment

of expected inflation rate from t-1 to t.

Likewise one would expect a positive relationship between stock returns and the

rate of inflation. But numerous studies have showed that common stock returns are

negatively correlated with inflation, the value of stocks decreases as inflation rises.

The study would be helpful to all investors, speculators, arbitragers, brokers,

dealers etc as the inflation can also be considered as one of the factors, which affect the

stock prices and in the same way stock prices as a factor affecting inflation rates.

Hypothesis of the study Hypothesis 1

H0: There is no significant relation between stock returns and inflation rates

H1: There is significant relation between stock returns and inflation rates

Hypothesis 2

H0: There is no significant lead and lag relationship between stock returns and

Inflation

H1: There is significant lead and lag relationship between stock returns and Inflation

Study Design

a) Study Type: The study type is analytical, quantitative and historical.

Analytical because facts and existing information is used for the analysis,

Quantitative as relationship is examined by expressing variables in

measurable terms

Historical as the historical information is used for analysis and

interpretation.

b) Study population: population is the indices of national stock exchange & 30 stocks of

Bombay stock exchange and wholesale price index.

c) Sampling frame: Sampling Frame is the Indian stock market.

d) Sample: Sample chosen is continuously compounded monthly closing values of BSE

Sensex, CNX Nifty, Bank Index, and the wholesale price index.

e) Sampling technique: Deliberate sampling is used because only particular units are

selected from the sampling frame. Such a selection is undertaken

as these units represent the population in a better way and reflect

better relationship with the other variable.

f) Period of the study: the period is different for different indices. CNX nifty is taken for

ten years from April, 1995 to March, 2005, Bank Index for five

years from January, 2000 to March, 2005 and BSE Sensex from

July, 1996 to March, 2005.

Data gathering procedures and instruments:

Data: Historical continuously compounded monthly share prices BSE Sensex, CNX nifty,

Bank Index. Monthly closing values of wholesale price index.

Data Source: Historical share prices of the NSE sample are taken from

www.nseindia.com and BSE from www.financeyahoo.com and wholesale

price index are taken from www.rbi.org.in

Software packages: various softwares packages like SSPS, Eview and Spreedsheet have

been used to run the statistical models. SSPS and spreadsheet is used

http://www.nseindia.com/

http://www.financeyahoo.com/

http://www.rbi.org.in/

for regression and Eview for calculating ADF Test, Grangers Co-

integration Test and Grangers causality Test.

Statistical Models

• Augmented Dicky Fuller test to test the stationary of the series.

• Granger’s cointegration approach to test for co integration between the series.

• Granger’s Causality test

Samples S&P CNX Nifty

S&P CNX Nifty is a well diversified 50 stock index accounting for 25 sectors of

the economy. It is used for a variety of purposes such as benchmarking fund portfolios,

index based derivatives and index funds.

Table No.1 S & P CNX Nifty Companies

Company name Industry Symbol Abb ltd. Electrical equipment Abb Associated cement companies ltd. Cement and cement products Acc Bajaj auto ltd. Automobiles - 2 and 3 wheelers Bajajauto Bharti tele-ventures ltd. Telecommunication - services Bharti Bharat heavy electricals ltd. Electrical equipment Bhel Bharat petroleum corporation ltd. Refineries Bpcl Cipla ltd. Pharmaceuticals Cipla Dabur india ltd. Personal care Dabur Dr. Reddy's laboratories ltd. Pharmaceuticals Drreddy Gail (india) ltd. Gas Gail Glaxosmithkline pharmaceuticals ltd. Pharmaceuticals Glaxo Grasim industries ltd. Cement and cement products Grasim Gujarat ambuja cements ltd. Cement and cement products Gujambcem Hcl technologies ltd. Computers - software Hcltech Housing development finance corporation ltd. Finance - housing Hdfc Hdfc bank ltd. Banks Hdfcbank Hero honda motors ltd. Automobiles - 2 and 3 wheelers Herohonda Hindalco industries ltd. Aluminium Hindalc0 Hindustan lever ltd. Diversified Hindlever Hindustan petroleum corporation ltd. Refineries Hindpetro Icici bank ltd. Banks Icicibank

http://www.nseindia.com/content/indices/ind_niftylist.htm

http://www.nseindia.com/content/indices/ind_niftytrading.htm

http://www.nseindia.com/content/indices/ind_indexfunds.htm

Infosys technologies ltd. Computers - software Infosystch Indian petrochemicals corporation ltd. Petrochemicals Ipcl I t c ltd. Cigarettes Itc Jet airways (india) ltd. Travel & transport Jetairways Larsen & toubro ltd. Engineering Lt Mahindra & mahindra ltd. Automobiles - 4 wheelers M&m Maruti udyog ltd. Automobiles - 4 wheelers Maruti Mahanagar telephone nigam ltd. Telecommunication - services Mtnl National aluminium co. Ltd. Aluminium Nationalum Oil & natural gas corporation ltd. Oil exploration/production Ongc Oriental bank of commerce Banks Orientbank Punjab national bank Banks Pnb Ranbaxy laboratories ltd. Pharmaceuticals Ranbaxy Reliance energy ltd. Power Rel Reliance industries ltd. Refineries Reliance Steel authority of india ltd. Steel and steel products Sail Satyam computer services ltd. Computers - software Satyamcomp State bank of india Banks Sbin Shipping corporation of india ltd. Shipping Sci Sun pharmaceutical industries ltd. Pharmaceuticals Sunpharma Tata chemicals ltd. Chemicals - inorganic Tatachem Tata motors ltd. Automobiles - 4 wheelers Tatamotors Tata power co. Ltd. Power Tatapower Tata steel ltd. Steel and steel products Tatasteel Tata tea ltd. Tea and coffee Tatatea Tata consultancy services ltd. Computers - software Tcs Videsh sanchar nigam ltd. Telecommunication - services Vsnl Wipro ltd. Computers - software Wipro Zee telefilms ltd. Media & entertainment Zeetele

CNX Bank Index

The Indian banking Industry has been undergoing major changes, reflecting a

number of underlying developments. Advancement in communication and information

technology has facilitated growth in internet-banking, ATM Network, Electronic transfer

of funds and quick dissemination of information. In order to have a good benchmark of

the Indian banking sector, India Index Service and Product Limited (IISL) has developed

the CNX Bank Index.

CNX Bank Index is an index comprised of the most liquid and large capitalized

Indian Banking stocks. It provides investors and market intermediaries with a benchmark

that captures the capital market performance of Indian Banks. The index will have 12

stocks from the banking sector which trade on the National Stock Exchange.

Table No.2 CNX Bank Index Companies

Company name Symbol Andhra bank Andhrabank Bank of baroda Bankbaroda Bank of india Bankindia Canara bank Canbk Corporation bank Corpbank Hdfc bank ltd. Hdfcbank Icici bank ltd. Icicibank Oriental bank of commerce Orientbank Punjab national bank ltd. Pnb State bank of india Sbin Syndicate bank Syndibank Union bank of india Unionbank

BSE Sensex Of the 23 stock exchanges in the India, Mumbai's (earlier known as Bombay),

Bombay Stock Exchange is the largest, with over 6,000 stocks listed. The BSE accounts

for over two thirds of the total trading volume in the country. Established in 1875, the

exchange is also the oldest in Asia. Among the twenty-two Stock Exchanges recognized

by the Government of India under the Securities Contracts (Regulation) Act, 1956, it was

the first one to be recognized.

SENSEX is a basket of 30 constituent stocks representing a sample of large,

liquid and representative companies. The base year of SENSEX is 1978-79 and the base

value is 100. The index is widely reported in both domestic and international markets

through print as well as electronic media.

Table No.3 BSE Sensex Companies

Company name Industry Reliance industries ltd. Refineries Infosys technologies ltd. Computer software Icici bank ltd. bank Itc ltd. Cigarettes Housing development finan Finance housing Larsen & toubro ltd. Engineering Hindustan lever ltd. Diversified Bharti tele-ventures ltd. Telecommunication - services Oil & natural gas corpora Oil exploration/production Tata steel ltd. Steel and steel products State bank of india bank Satyam computer services Computer software Hdfc bank ltd. bank Bajaj auto ltd. Automobiles - 2 and 3 wheelers Tata motors ltd. Automobiles - 4 wheelers Tata consultancy services Computer software Bharat heavy electricals Electrical equipment Hindalco industries ltd. Aluminium Ntpc ltd Grasim industries ltd. Cement and cement products Wipro ltd. Computer software Gujarat ambuja cements lt Cement and cement products Associated cement compani Cement and cement products Ranbaxy laboratories ltd. Pharmaceuticals Cipla ltd. Pharmaceuticals Maruti udyog ltd. Automobiles - 4 wheelers Hero honda motors ltd. Automobiles - 2 and 3 wheelers Dr. Reddy's laboratories Pharmaceuticals Tata power company ltd. Power Reliance energy ltd. Power

Limitations of the study

• The results may not give accurate picture as there could be many other macro

factors other than inflation which affects the stock returns at the same period.

• The study is limited to only three indices.

http://www.contentlinks.asiancerc.com/ib/LiveDetailedQuotes.asp?CompanyCode=12150008&Exchange=BSE%20






























Statistical Models

In this study, a co-integration approach using the Engle-Granger methodology is

applied to capture both the long run and the short run dynamics of stock returns and

inflation rates. Before doing co-integration analysis, it is necessary to test whether the

time series are stationary at levels by running Augmented Dickey fuller (ADF) test on the

series. Because most time series are non stationary in levels, and the original data need to

be transformed to obtain stationary series. And then the granger causality test is done to

test the causal relationship between stock returns and inflation.

Stationarity

According to Engle and Granger, a time series is said to be stationary if

displacement over time does not alter the characteristics of a series in a sense that

probability distribution remains constant over time. In other words, the mean, variance

and co-variance of the series should be constant over time. A nonstatioanry time series

will have a time varying mean or a time varying variance or both or are

autocorrelated.The degree of co-integration is closely related with stationary.

It is evident from the time-series literature that the standard estimation and

statistical test procedures are highly inappropriate, and even invalid, when the variables

involved are nonstationary.

The empirical works based on time series data assumes that the underlying time

series is stationary. In regressing a time series variable on another time series variables,

one often obtains a very high R2 (residuals) even though there is no meaningful

relationship between the two variables. This situation exemplifies the problem of

spurious or nonsense regression, which arises when data is non stationary.

A series is said to be integrated of order one [I (1)] if it has to be differentiated

once before becoming stationary. Similarly, a series is of order two [I(2)] if it has to be

differentiated twice before becoming stationary.

Theory of Stationarity

Following are different ways of examining about whether a time series variable Xt is

stationary or has a unit root

• In the AR(1) model, if Φ=1, then X has a unit root. If |Φ| <1 then X is stationary.

• If X has a unit root then the series will exhibit trend behavior.

• If X has a unit root, then ΔX will be stationary. For this reason, series with unit root

are often referred to as difference stationary series

• Series is stationary if durbin Watson values lies between 1.5 to 2.5, which indicates

that there is no autocorrelation

Testing Stationarity

In general, the procedure start with whether the variables Y in its level form is

stationary. If the hypothesis is rejected, then the series is transformed into first difference

of the variable and tested for stationarity. If first difference series is stationary, this

implies that Y is I(1).

H0: Series has Unit root : Non Stationary

H1: Series does not have Unit root : Stationary

Unit Root Test [Dickey Fuller Test]:

Dickey Fuller test involve estimating regression equation and carrying out the

hypothesis test. The AR (1) process is….

Yt = C + ρYt-1+ εt

Where c and ρ are parameters and is to be white noise. If -1 < ρ < 1, then Y is

stationary series . While ρ if = 1, y is non stationary series. Therefore, why not simply

regress Yt on its lagged value yt-1 and find out if the estimated ρ is statistically equal to 1

? If it is, then Yt is nonstationary this is the general idea behind the unit root test of

stationarity. The test is carried out by estimating an equation with Yt-1 subtracted from

both sides of the equation.

Δyt = C + γt-1 + εt

Where δ = (ρ-1), and the null and alternative hypotheses are

Ho: δ = 0 …..Non Stationary

H1: δ < 0 …..Stationary

Dickey and fuller simulated the critical values for selected sample sizes. More

recently, Mackinnon (19991) has implemented a much larger set of simulations than

those tabulated by Dickey and Fuller.

Unit root test [Augmented dickey fuller test]

The simple Unit root test is valid only if the series is an AR (1) Process. If the

series is correlated at high order lags, the assumption of white noise disturbances is

violated. [In other words, in DF test,it was assumed that the error term εt was

uncorrelated. But in case the error ter mis correlated, Dickey and Fuller have developed a

test, knoen as Augmented Dickey Fuller test]. The ADF controls for higher - order

correlation by adding lagged difference terms of the dependent variable to the right-hand

side of the regression

ΔYt = C + γt-1 + δ1Δ yt-1 + δ2Δ y t-2 + …..+ δpΔ y t-p + εt

This augmented specification is then tested

H0: δ = 0 Non Stationary

H1: δ < 0 Stationary

The unit root test is based on the following three regression forms:

1. Without intercept and trend (random walk) ΔYt = δYt-1 + εt

2. with intercept (random walk with drift) ΔYt = α + δYt-1 +εt

3. with intercept and trend (with drift around a stochactic trend) ΔYt = α βT + δYt-1 +εt

Where, α is the intercept/constant, T is trend, β is the slope i.e level of

dependency and integration, δ is drift parameter i.e. Change from Yt to Yt-1 and εt is the

error term.

In general, the procedure start with whether the variables X and Y in its level

form under none, intercept and trend and intercept is stationary. If the hypothesis is

rejected, then the series is transformed into first difference of the variable and tested for

stationarity. If first difference series is stationary, this implies that X and Y are I(1).

Grangers co-integration Test

The fundamental aim of co integration analysis is to detect any common

stochastic trends in the price data and to use these common trends for a dynamic analysis

of correlation in returns. Correlation is based only on return data, but full co integration

analysis is based on the raw prices, rate or yield as well as return data.

According to co integration theory, two variables that are stationary in changes

are co integration if a linear combination of them in levels is stationary. Thus, changes in

the prices are taken for running the test.

Granger introduced the concept of co-integration when he wrote that two

variables may move together though individually they are non stationary. Co-integration

is based on the long run relationship between variables. The idea arises from considering

equilibrium relationships, where equilibrium is a stationary point characterized by forces

that tend to push the variables back toward equilibrium.

In general, if Yt and Xt are both integrated of order I (d), then any linear

combination of the two series will also be I (d)... That is, the residuals obtained on

regressing Yt on Xt are I (d).

If two or more series are co integrated then even though the series themselves

may be non stationary, they will move closely together over time and their difference will

be stationary. Their long run relationship is the equilibrium to which the system

converges overtime and the disturbance term Et can be construed as the disequilibrium

error or the distance that the system is away from equilibrium at time t.

The Engle granger test is a two step process:

• First estimating an ordinary least square (OLS) regression on the data. A

regression of one integrated variable on the other integrated variables (x on y and

y on x).

Yt = a + bx t + e tX & y will be co-integrated if and only if e is stationary.

• Then test the residuals from regression for stationarity using a unit root test such

as ADF.

Grangers Causality Test

The relationship between co integration and causality arises from the fact that, if

two variables are co-integrated, then causality must exist in at least one direction and

possibly in both directions.

Although regression analysis deals with the dependence of one variable on other

variables, it does not necessarily imply causation. In other words, the existence of a

relationship between variables does not prove causality or direction of influence. More

generally, since the future cannot predict the past, if variable X causes variable Y, then

changes in X should precede changes in Y.

Granger causality is a technique for determining whether one time series is useful

in forecasting another. Ordinarily, regressions reflect "mere" correlations, but Clive

Granger causality test shows about the causality between two series.

It measures the significance of past values of variable X in explaining variable Y,

taking into account the effect of past values of variable Y itself. Usually causal relations

are tested both ways, from X to Y and from Y to X.

A time series X is said to Granger-cause Y if it can be shown, usually through a

series of F-tests on lagged values of X (and with lagged values of Y also known), that

those X values provide statistically significant information on future values of Y.

http://en.wikipedia.org/wiki/F-test

The test works by first doing a regression of ΔY on lagged values of ΔY. Once

the appropriate lag interval for Y is proved significant (t-stat or p-value), subsequent

regressions for lagged levels of ΔX are performed and added to the regression provided

that they 1) are significant in of themselves and 2) add explanatory power to the model.

This can be repeated for multiple ΔX's (with each ΔX being tested independently of other

ΔX's, but in conjunction with the proven lag level of ΔY). More than 1 lag level of a

variable can be included in the final regression model, provided it is statistically

significant and provides explanatory power.

The steps involved in implementing the Granger causality are:

• Restricted Residual Sum of squares (RSSR):

Regress X on all lagged X, but do not include the lagged y variables in thie

regression.

• Unrestricted residual sum of squares (RSSUR):

Now run the regression including the lagged Y terms.

• The null hypothesis is, lagged Y terms do not belong in the regression.

• To test this hypothesis, apply F test:

F = (RSSR – RSSUR)/Y

RSSR / (n-k)

Y is equal to the number of lagged Y terms and k is the number of parameters

estimated in the unrestricted regression.

If the computed f value exceeds the critical F values at the chosen level of

significance, we reject the null hypothesis, in which case the lagged y belongs in

the regression. i.e. Y causes X.

The whole procedure should be repeated to test whether X causes Y.

Empirical Results Unit Root Test [Augmented Dickey Fuller Test] Wholesale Price Index:

The wholesale price index for this test is taken from April 1995 to March 2005.

The data taken are raw prices. The unit root result is as under:

Table No 4: WPI ADF Test

Constraints

( log 0 )

ADF values Mackinnon

Critical values

None

( level)

7.763608** 1% (-2.5830)

5% (-1.9426)

10% (-1.6171)

Intercept

(level)

0.893221** 1% (-3.4861)

5% (-2.8857)

10% (-2.5795)

Trend & intercept

(level)

-2.101586** 1% (-4.0373)

5% (-3.4478)

10% (-3.1488)

None

( 1st difference)

-6.321485* 1% (-2.5831)

5% (-1.9427)

105 (-1.6171)

** indicates acceptance of null hypothesis

* indicates rejection of null hypothesis

( * reference no.1,2,3 & 4)

Hypothesis:

H0 = ADF > critical values -- not reject hull hypothesis i.e., unit root exists.

H1 = ADF < critical values – reject null hypothesis i.e. unit root does not exist.

* for reference no 1,2,3 & 4 – see annexure)

Interpretation

The above table tells that WPI series has a unit root problem, so they are non

stationary in their level at various constraints i.e ADF is greater than critical values at

none intercept and trend & intercept. The null hypothesis is accepted at 1%, 5% and 10%

level of significance.

However, the series at 1st difference level is stationary as ADF is greater than

critical value the 1st difference level is nothing but the log natural returns of the raw

prices. Log natural returns are to make series mean and variance constant. Thus WPI

series is stationary at I (1) and null hypothesis is rejected at 1%, 5% and 10% level of

significance.

Graph no. 1

WPI Movements from April,1995 to March,2005

0

50

100

150

200

1 11 21 31 41 51 61 71 81 91 101 111

No.of observation

WPI

pric

es

Interpretation

The stationarity of a series can also be presented graphically. The graph above

indicates the rough idea on whether a time series is stationary or not. The series seems as

a non stationary data since it is increaseing upward as time changes.

S & P CNX Nifty:

The CNX Nifty prices are taken from April, 1995 to March, 2005. The data taken

are raw closing prices. The unit root result is as under:

Table No. 5 Nifty ADF Test

Constraints

( log 0)


Critical values

None

( level)

0.985726** 1% (-2.5830)

5% (-1.9426)

10% (-1.6171)

Intercept

(level)

-0.497179** 1% (-3.4861)

5% (-2.8857)

10% (-2.5795)

Trend & intercept

(level)

-1.480579** 1% (-4.0373)

5% (-3.4478)

10% (-3.1488)

None

( 1st difference)

-10.60268* 1% (-2.5831)

5% (-1.9427)

105 (-1.6171)

(** indicates acceptance of null hypothesis)

(* indicates rejection of null hypothesis)

(*reference no.5,6,7 & 8)

Hypothesis:



(* for reference no.5,6,7 & 8 – see annexure)

Interpretation

The CNX Nifty results from the table shows unit root problem, so they are non

stationary at their at none, intercept and trend & intercept i.e. ADF is greater than

Mackinnon critical values of 1%,5% and 10% level of significance. However, their 1st

difference is stationary as ADF is greater than critical value. The null hypothesis is

rejected for (-10.60268) at 1%,5% and 10% level of significance.

Graph no 2

0

500

1000

1500

2000

2500

1 12 23 34 45 56 67 78 89 100 111

No of observation

Nift

y pr

ices

NIFTY Movements from April,1995 to March,2005

Interpretation

The graph above speaks about the stationarity of the eries. It indicates whether a

time series is stationary or not. Nifty movements from April, 1995 to March, 2005

showing upward trend. Thus the series seems as a non stationary data since it is

increaseing upward as time changes.

Bank Index:

The Bank index is for the period January 2000 to march, 2005. The Data taken are

raw closing prices. The unit root result is as under:

Table No.6 Bank Index ADF Test:

Constraints

( log 0)


Critical values

None

( level)

1.909361** 1% (-2.6000)

5% (-1.9457)

10% (-1.6185)

Intercept

(level)

0.807602** 1% (-3.5380)

5% (-2.9084)

10% (-2.5915)

Trend & intercept

(level)

-1.526356** 1% (-4.1109)

5% (-3.4824)

10% (-3.1689)

None

( 1st difference)

-7.624449* 1% (-2.6006)

5% (-1.9458)

105 (-1.6186)

(** indicates acceptance of hypothesis)

(* indicates rejection of hypothesis)

(*reference 9,10,11 & 12)

Hypothesis:



(* for reference no.9,10,11 & 12– see annexure)

Interpretation

The table above shows that the Bank index is non stationary and their exists the

unit root problem as ADF is greater than Mackinnon critical values. Thus the null

hypothesis is rejected at 1%,5% and 10% level of significance.

However, they are stationary at I (1) where null hypothesis is rejected at various

level of significance 1%,5% and 10%.

Graph No 3

BANK INDEX Movements from January,2000 to March,2005.

0

1000

2000

3000

4000

1 6 11 16 21 26 31 36 41 46 51 56 61

No of observations

bank

Inde

x pr

ices

Interpretation

The bank index movements are moving upward as time changes, indicating the

non stationarity nature of the series. Thus it can be concluded from the graph that the

bank index time series are non stationary.

BSE Sensex:

The BSE sensex is taken from January 1996 to March, 2005. The data taken for

ADF are the raw closing prices. The unit root result is as under:

Table no.7 BSE Sensex ADF Test

Constraints

( log 0)


Critical values

None

( level)

0.847576** 1% (-2.5856)

5% (-1.9431)

10% (-1.6173)

Intercept

(level)

-0.340429** 1% (-3.4940)

5% (-2.8892)

10% (-2.5813)

Trend & intercept

(level)

-1.086908** 1% (-4.0485)

5% (-3.4531)

10% (-3.1519)

None

( 1st difference)

-9.896049* 1% (-2.5858)

5% (-1.9432)

10% (-1.6174)

(** indicates acceptance of null hypothesis)


(* Reference no.13,14,15 & 16)

Hypothesis:



(* for reference no.13,14,15 & 16 – see annexure)

Interpretation

The ADF in none, intercept and trend are greater than Mackinnon critical value

indicating that the BSE sensex series are non stationary at their levels. Thus the null

hypothesis is accepted at 1%,5% and 10% level of significance. However the series are

stationary at I(1) at 1%,5% and 10% level of significance.

Graph No.4

0.001000.002000.003000.004000.005000.006000.007000.008000.00

1 12 23 34 45 56 67 78 89 100

No of observations

BSE

Sen

sex

Pric

es

BSE Sensex Movements from July,1996 to March,2005

Interpretation

The above graph of BSE Sensex is moving upward from January 1996 to march,

2005, indicating the non stationarity of the series.

Grangers Co integration Test: Co integration between CNX Nifty and Whole sale price index • An ordinary least square (OLS) regression is done on the data. First x is regressed

on y then y on x.

X on Y -- Dependent variable(X) is CNX Nifty and Independent variable(Y) is WPI.

Y on X -- Dependent variable(X) is WPI and Independent variable(Y) is CNX Nifty.

NIFTYt = a + bWPI t + e t ………………………………….. (1)

WPIt = a + bNIFTY t + e t ………………………………….. (2)

Table No.8 Nifty and WPI Regression Result

Parameter (1) (2)

Coefficient -3.027 -0.0174

(* Reference table 17)

• Residuals e=y-y^ Results

The residuals of both the regression equations are stationary.

Table No.9 Nifty and WPI Co-integration Test:

Constraints

(log 0)


Critical values

None ( level)

[X on Y]

-12.08444*

1% (-2.530)

5% (-1.9426)

10% (-1.6171)

None ( level)

[Y on X]

-11.13941*

1% (-2.530)

5% (-1.9426)

10% (-1.6171)


(* Reference no.18 & 19)

(*for reference 17,18 & 19 -see annexure)

Interpretation:

Unit root test for stationarity of residuals from the co integration equation shows

that the null hypothesis is rejected at 1%,5% and 10% level of significance implying

CNX Nifty and WPI are co integrated, but as the coefficients are statistically significant

with a negative sign. This indicates the negative relationship between CNX Nifty and

WPI.

Co integration between Bank Index and Whole sale price index • An ordinary least square (OLS) regression is done on the data. First x is regressed on

y then y on x.

X on Y -- Dependent variable(X) is Bank Index and Independent variable(Y) is WPI.

Y on X -- Dependent variable(X) is WPI and Independent variable(Y) is bank Index.

BANKt = a + bWPI t + e t ………………………………….. (1)

WPIt = a + bBANK t + e t ………………………………….. (2)

Table No.10 Bank Index and WPI Regression Results

Parameter (1) (2)


(* Reference 20)



Table No.11 Bank Index and WPI co-integration Test

Constraints

( log 0)


Critical values

None ( level)

[X on Y]

-8.611257*

1% (-2.6000)

5% (-1.9457)

10% (-1.6185)

None ( level)

[Y on X]

-7.523411*

1% (-2.6000)

5% (-1.9457)

10% (-1.6185)



(* for reference no.20,21 & 22 – see annexure)

Interpretation:

The Bank Index and WPI are correlated as Unit root test for stationarity of

residuals from the co integration equation shows that the null hypothesis is rejected at

1%,5% and 10% level of significance, implying Bank Index and WPI are co integrated,

but as the coefficients are statistically significant with a negative sign. This indicates the

negative relationship between CNX Nifty and WPI.

Co integration between BSE Sensex and Whole sale price index • An ordinary least square (OLS) regression is done on the data. First x is regressed on

y then y on x.

X on Y -- Dependent variable(X) is BSE Sensex and Independent variable(Y) is WPI.

Y on X -- Dependent variable(X) is WPI and Independent variable(Y) is BSE Sensex.

BSEt = a + bWPI t + e t ………………………………….. (1)

WPIt = a + bBSE t + e t ………………………………….. (2)

Table No.12 BSE Sensex and WPI Regression Results

Parameter (1) (2)


(* Reference no.23)



Table No.13 BSE Sensex and WPI co-integration Test

Constraints ADF values Critical values

None ( level)

[X on Y]

-10.56767*

1% (-2.5856)

5% (-1.9431)

10% (-1.6173)

None ( level)

[Y on X]

-10.34426*

1% (-2.6000)

5% (-1.9431)

10% (-1.6173)

(* indicates rejection of hull hypothesis)


(* for reference no.23,24, & 25 – see annexure)

Interpretation:

The BSE Sensex and WPI are co integrated but they have a negative relationship as

coefficients are negative. Unit root test for stationarity of residuals from the co

integration equation shows that the null hypothesis is rejected at 1%,5% and 10% level of

significance.

Grangers Causality Test:

CNX Nifty and Whole sale price index The causality results for the two variables are:

Table No.14 Nifty and WPI causality Test

Lags Hypothesis No of

observations F statistics ProbabilityWPI does not causes Nifty 3.33728 0.03910

2 Nifty does not causes WPI 117 3.37426 0.03776WPI does not causes Nifty 3.64799 0.00797




24 Nifty does not causes WPI 95 1.00842 0.47574 (* Reference no.26)

H0 = NIFTY does not causes WPI

H1 = NIFTY causes WPI

H0 =WPI does not causes NIFTY

H1 = WPI causes NIFTY

Interpretation

The calculated F values from lag 2 to 24 are greater than the F statistics, which

rejects the null hypothesis. And the P value is also close to zero. Thus there is

bidirectional causality at every lag between CNX Nifty and WPI.

( * for reference no.26 see annexure)

Bank Index and Whole sale price index The causality results for the two variables are:

Table No.15 Bank Index and WPI causality Test


observations F statistics ProbabilityWPI does not causes Bank 3.22490 0.04739

2 Bank does not causes WPI 60 6.43431 0.00308WPI does not causes Bank 3.03205 0.02599



12 Bank does not causes WPI 50 1.18496 0.34507 (* Reference 27)

H0 = BANK does not causes WPI

H1 = BANK does WPI

H0 =WPI does not causes BANK

H1 =WPI causes BANK

Interpretation

As the P value is close to zero and the calculated F values from lag 2 to 12 are

greater than the F statistics, the null hypothesis is rejected. Thus there is bidirectional

causality between bank indexes to WPI.

(* for reference no.27 – see annexure)

BSE Sensex and Whole sale price index The causality results for the two variables are:

Table No.16 BSE Sensex and WPI causality Test


observations F statistics ProbabilityWPI does not causes BSE 5.38766 0.00603

2 BSE does not causes WPI 103 2.93531 0.05780WPI does not causes BSE 3.11784 0.01874




24 BSE does not causes WPI 81 1.22307 0.29359 (* Reference no.28)

H0 = BSE does not causes WPI

H1 = BSE causes WPI

H0 =WPI does not causes BSE

H1 = WPI causes BSE

Interpretation

The calculated F values from lag 2 to 24 are greater than the F statistics, which

rejects the null hypothesis. The P values are also close to zero. Thus there is bidirectional

causality between bank indexes to WPI.

(* for reference no.28 – see annexure)

Conclusions

The Fisher hypothesis states that the real rates of returns on common stock and

expected inflation rates are independent and the nominal stock returns vary in one to one

correspondence with expected inflation. The expected nominal returns contain market

assessments of expected inflation rates. Thus, if the market is efficient processor of the

information available, it will set the prices so that the expected nominal return is the sum

of the expected real return and the best possible assessment of the expected inflation.

As the index is nothing but weighted average of the share prices of various

companies from different sectors, the sensex has been considered to see the impact of

inflation on it. Sensex, Nifty and Bank index are considered to see where they move in

the same direction or not.

After analyzing the data using the various Grangers test, it has been found that

there is no positive relationship between stock returns and inflation. The results of the

three indices are:

• CNX Nifty:

The nifty is considered for a period of 10 years. The series is stationary at I(1), but

it is negatively related to inflation as the coefficients statistically significant with negative

sign (-3.027) & (-0.01742) And there exists a bidirectional causal relationship between

nifty and inflation.

• Bank Index:

The bank index is considered for 5 years. The series is stationary at I(1). And its

coefficient is also statistically significant with a negative sign (-3.643) & (-0.01515).

Thus showing the negative relation between the two. Its causal relationship is in both

directions.

• BSE Sensex:

The results of BSE are also same as nifty and bank. Study is done for a period of 9

years. It is stationary at I (1) and its coefficients are (-3.146) & (-0.01692) showing the

negative relation. And the causality runs from the both direction.

Thus, the relationship between stock returns and inflation does not change with

indices.

It is evident from the overall results that the causality runs from inflation to stock

returns and also in the reverse order with a negative sign in both directions. The

coefficients are statistically significant with a negative sign.

The negative relationship can be interpreted several ways: for example the

unexpected inflation is generally considered to be positively correlated with inflation

uncertainty and high level of inflation uncertainty discourages investments in risky assets

and results in reduced nominal returns. Another interpreted is that the negative relation is

due to the fact that changes in expected inflation are most likely to be positively

correlated with unexpected inflation.

The market informational efficiency hypothesis can be rejected, as there exists a

bidirectional relationship between stock returns and inflation. The market is

informationally inefficient with respect to the rate of inflation. The market participants

can develop profitable trading rules and thereby can consistently earn more than average

market returns, as future inflation can be predicted.

It can be concluded that the stocks are a perverse inflation hedge. This does not

mean that equities are hazardous to investors’ health. Stocks are priced today to yield

very lucrative returns. The prospective returns have to be good; however, to compensate

stockholders for the risk they bear because equities are a perverse inflation hedge. When

the rate of inflation unexpected increases, real stock prices will fall. Conversely, when the

rate of inflation unexpectedly drops, real stock prices will raise.

So if one does not mind bearing some risk especially the risk that the inflation rate

may be higher than stocks are a good investment. If one seeks an inflation hedge, stocks

are generally poor investments. My conclusion rests on the observation that rising

inflation rates tend to depress corporate earnings and thereby stock prices, which has

been proved by the Grangers co integration test.

Thus, if one wants to cover the stock price risks, he can go for derivative market.

If an investor is having underlying asset and wants to protect himself from unexpected

inflation movements, he can enter the future market by entering into long and short

contracts based on future predictions.

Bibliography

TEXT BOOKS:

• Basic Econometrics

- Damodar N.Gujarati, (fourth edition)

• Macroeconomics

- Mishra and Puri

• Multinational Financial Management

- Alan C. Shapiro ( seventh edition)

REFERENCE BOOKS

• Inflation in India – Indian Institute of Management, Bangalore

• Market Models -- Indian Institute of Management, Bangalore.

WEB SITES:

• www.nseindia.com

• www.financeyahoo.com

• www.inflationdata.com

• www.google.com

• www.investorpedia.com

• www.bseindia.com

• www.rbi.org.in

ARTICLES:

• Stock market and macro economic behaviour in India

-- Sangeeta Chakravarty, Institute of Economic growth, University

Enclave, Delhi.

• An overview of the impact of inflation on the stock market

-- Richard T.Coghlam and J.Anthony Boekh.

• How Inflation Swindles the equity investors

-- Warren E.Buffett

http://www.nseindia.com/

http://www.financeyahoo.com/

http://www.inflationdata.com/

http://www.google.com/

http://www.investorpedia.com/

http://www.bseindia.com/

http://www.rbi.org.in/

• Stocks are not an Inflation hedge

-- Richard W.Kopcke

• The Mythes of common stocks and Inflation

-- Steven C.Leuthold

• Inflation and the stock market

-- Franco Modigliani and Richard A.Cohn

REFERENCES:

1. Fama and Schwartz (1977), “Asset Returns and Inflation”, Journal of Financial

economics, Vol.5, November, pp. 115-46.

2. Jacob Boudoukh and Matthew Richardson (1993), “Stock returns and Inflation:

A long horizon Perspective”, American Economic review, Vol.83, pp. 1346-

1355.

3. Gultekin, N B (1983), “Stock market Returns and Inflation: evidence from other

countries”, The Journal of Finance, Vol. 38, No.1 (March), pp. 49-65.

4. Bharat Kolluri (2005), “Stock Market returns and Inflation: An Analysis of the

Direction of Causality”. The ICFAI University Press.

5. Basabi Bhattacharya and Jaydeep Mukherjee,(2005) “ the Nature of the causal

Relationship between Stock Market and Macroeconomic Aggregates in India:

An Empirical Analysis”, JEL Classification: GI, E4.

Reference No. 1 -- ADF Unit Root Test on WPI [none]

ADF Test Statistic 7.763608 1% Critical Value* -2.5830

5% Critical Value -1.9426 10% Critical Value -1.6171

*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation Dependent Variable: D(WPI) Method: Least Squares Date: 06/11/06 Time: 20:44 Sample(adjusted): 1995:05 2005:03 Included observations: 119 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. WPI(-1) 0.003893 0.000501 7.763608 0.0000

R-squared 0.006599 Mean dependent var 0.589916 Adjusted R-squared 0.006599 S.D. dependent var 0.837105 S.E. of regression 0.834339 Akaike info criterion 2.484013 Sum squared resid 82.14227 Schwarz criterion 2.507367 Log likelihood -146.7988 Durbin-Watson stat 1.530646

Reference No. 2 -- ADF Unit Root Test on WPI [intercept]

ADF Test Statistic 0.893221 1% Critical Value* -3.4861 5% Critical Value -2.8857 10% Critical Value -2.5795



Variable Coefficient Std. Error t-Statistic Prob. WPI(-1) 0.003359 0.003761 0.893221 0.3736

C 0.082203 0.573573 0.143317 0.8863 R-squared 0.006773 Mean dependent var 0.589916 Adjusted R-squared -0.001716 S.D. dependent var 0.837105 S.E. of regression 0.837823 Akaike info criterion 2.500644 Sum squared resid 82.12786 Schwarz criterion 2.547352 Log likelihood -146.7883 F-statistic 0.797843 Durbin-Watson stat 1.530092 Prob(F-statistic) 0.373573

Reference No. 3-- ADF Unit Root Test on WPI [trend & intercept]

ADF Test Statistic -2.101586 1% Critical Value* -4.0373 5% Critical Value -3.4478 10% Critical Value -3.1488



Variable Coefficient Std. Error t-Statistic Prob. WPI(-1) -0.071890 0.034208 -2.101586 0.0378

C 8.755472 3.960056 2.210946 0.0290 @TREND(1995:04) 0.045001 0.020337 2.212766 0.0289

R-squared 0.046999 Mean dependent var 0.589916 Adjusted R-squared 0.030568 S.D. dependent var 0.837105 S.E. of regression 0.824212 Akaike info criterion 2.476108 Sum squared resid 78.80166 Schwarz criterion 2.546170 Log likelihood -144.3284 F-statistic 2.860373 Durbin-Watson stat 1.480420 Prob(F-statistic) 0.061294

Reference No4 -- ADF Unit Root Test on WPI [1st difference level]



Augmented Dickey-Fuller Test Equation Dependent Variable: D(WPI,2) Method: Least Squares Date: 06/11/06 Time: 20:53 Sample(adjusted): 1995:06 2005:03 Included observations: 118 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. D(WPI(-1)) -0.507888 0.080343 -6.321485 0.0000

R-squared 0.254591 Mean dependent var -0.001695 Adjusted R-squared 0.254591 S.D. dependent var 1.034613 S.E. of regression 0.893255 Akaike info criterion 2.620549 Sum squared resid 93.35481 Schwarz criterion 2.644029 Log likelihood -153.6124 Durbin-Watson stat 2.123953

Reference No. 5 -- ADF Unit Root Test on Nifty [none]




Augmented Dickey-Fuller Test Equation Dependent Variable: D(NIFTY) Method: Least Squares Date: 06/11/06 Time: 20:55 Sample(adjusted): 1995:05 2005:03 Included observations: 119 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. NIFTY(-1) 0.006447 0.006540 0.985726 0.3263

R-squared -0.002772 Mean dependent var 9.191765 Adjusted R-squared -0.002772 S.D. dependent var 87.89297 S.E. of regression 88.01472 Akaike info criterion 11.80125 Sum squared resid 914097.7 Schwarz criterion 11.82461 Log likelihood -701.1746 Durbin-Watson stat 1.982355

Reference No. 6-- ADF Unit Root Test on Nifty [intercept]




Variable Coefficient Std. Error t-Statistic Prob. NIFTY(-1) -0.013523 0.027200 -0.497179 0.6200

C 25.38429 33.55681 0.756457 0.4509 R-squared 0.002108 Mean dependent var 9.191765 Adjusted R-squared -0.006421 S.D. dependent var 87.89297 S.E. of regression 88.17469 Akaike info criterion 11.81318 Sum squared resid 909648.8 Schwarz criterion 11.85989 Log likelihood -700.8843 F-statistic 0.247187 Durbin-Watson stat 1.952699 Prob(F-statistic) 0.619995

Reference No. 7 -- ADF Unit Root Test on Nifty [trend & intercept]




Variable Coefficient Std. Error t-Statistic Prob. NIFTY(-1) -0.051225 0.034598 -1.480579 0.1414

C 39.28946 34.21688 1.148248 0.2532 @TREND(1995:04) 0.520631 0.299301 1.739487 0.0846

R-squared 0.027476 Mean dependent var 9.191765 Adjusted R-squared 0.010709 S.D. dependent var 87.89297 S.E. of regression 87.42111 Akaike info criterion 11.80424 Sum squared resid 886524.2 Schwarz criterion 11.87430 Log likelihood -699.3521 F-statistic 1.638640 Durbin-Watson stat 1.929766 Prob(F-statistic) 0.198708

Reference No. 8 -- ADF Unit Root Test on Nifty [1st difference level]



Augmented Dickey-Fuller Test Equation Dependent Variable: D(NIFTY,2) Method: Least Squares Date: 06/11/06 Time: 20:59 Sample(adjusted): 1995:06 2005:03 Included observations: 118 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. D(NIFTY(-1)) -0.980829 0.092508 -10.60268 0.0000

R-squared 0.489975 Mean dependent var -1.043814 Adjusted R-squared 0.489975 S.D. dependent var 124.0451 S.E. of regression 88.58815 Akaike info criterion 11.81431 Sum squared resid 918199.7 Schwarz criterion 11.83779 Log likelihood -696.0444 Durbin-Watson stat 1.994521

Reference No. 9-- ADF Unit Root Test on Bank Index [none]




Augmented Dickey-Fuller Test Equation Dependent Variable: D(BANK) Method: Least Squares Date: 06/11/06 Time: 21:01 Sample(adjusted): 2000:02 2005:03 Included observations: 62 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. BANK(-1) 0.024430 0.012795 1.909361 0.0609

R-squared 0.010723 Mean dependent var 38.51210 Adjusted R-squared 0.010723 S.D. dependent var 176.4821 S.E. of regression 175.5333 Akaike info criterion 13.18953 Sum squared resid 1879528. Schwarz criterion 13.22384 Log likelihood -407.8755 Durbin-Watson stat 2.131805

Reference No. 10 -- ADF Unit Root Test on Bank Index [intercept]

ADF Test Statistic 0.807602 1% Critical Value* -3.5380 5% Critical Value -2.9084 10% Critical Value -2.5915



Variable Coefficient Std. Error t-Statistic Prob. BANK(-1) 0.023319 0.028875 0.807602 0.4225

C 2.163314 50.30884 0.043001 0.9658 R-squared 0.010753 Mean dependent var 38.51210 Adjusted R-squared -0.005734 S.D. dependent var 176.4821 S.E. of regression 176.9873 Akaike info criterion 13.22176 Sum squared resid 1879470. Schwarz criterion 13.29038 Log likelihood -407.8745 F-statistic 0.652221 Durbin-Watson stat 2.129512 Prob(F-statistic) 0.422510

Reference No. 11 -- ADF Unit Root Test on Bank Index [trend & intercept]

ADF Test Statistic -1.526356 1% Critical Value* -4.1109




Variable Coefficient Std. Error t-Statistic Prob. BANK(-1) -0.078832 0.051647 -1.526356 0.1323

C -4.805393 48.60710 -0.098862 0.9216 @TREND(2000:01) 5.276050 2.246611 2.348448 0.0222

R-squared 0.095321 Mean dependent var 38.51210 Adjusted R-squared 0.064654 S.D. dependent var 176.4821 S.E. of regression 170.6816 Akaike info criterion 13.16465 Sum squared resid 1718800. Schwarz criterion 13.26758 Log likelihood -405.1043 F-statistic 3.108256 Durbin-Watson stat 2.103689 Prob(F-statistic) 0.052070

Reference No. 12 -- ADF Unit Root Test on Bank Index [1st difference level]



Augmented Dickey-Fuller Test Equation Dependent Variable: D(BANK,2) Method: Least Squares Date: 06/11/06 Time: 21:02 Sample(adjusted): 2000:03 2005:03 Included observations: 61 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. D(BANK(-1)) -0.987757 0.129551 -7.624449 0.0000

R-squared 0.492085 Mean dependent var -1.083934 Adjusted R-squared 0.492085 S.D. dependent var 255.2891 S.E. of regression 181.9398 Akaike info criterion 13.26149 Sum squared resid 1986125. Schwarz criterion 13.29609 Log likelihood -403.4753 Durbin-Watson stat 1.987823

Reference No. 13-- ADF Unit Root Test on BSE Sensex [none]




Augmented Dickey-Fuller Test Equation Dependent Variable: D(BSE) Method: Least Squares Date: 06/11/06 Time: 21:10 Sample(adjusted): 1996:07 2005:02 Included observations: 104 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. BSE(-1) 0.006124 0.007226 0.847576 0.3986

R-squared -0.001734 Mean dependent var 27.89750 Adjusted R-squared -0.001734 S.D. dependent var 300.1898 S.E. of regression 300.4499 Akaike info criterion 14.25801 Sum squared resid 9297827. Schwarz criterion 14.28344 Log likelihood -740.4164 Durbin-Watson stat 1.968782

Reference No. 14-- ADF Unit Root Test on BSE Sensex [intercept]




Variable Coefficient Std. Error t-Statistic Prob. BSE(-1) -0.011130 0.032693 -0.340429 0.7342

C 72.14834 133.3051 0.541227 0.5895 R-squared 0.001135 Mean dependent var 27.89750 Adjusted R-squared -0.008658 S.D. dependent var 300.1898 S.E. of regression 301.4865 Akaike info criterion 14.27437 Sum squared resid 9271202. Schwarz criterion 14.32522 Log likelihood -740.2673 F-statistic 0.115892 Durbin-Watson stat 1.940570 Prob(F-statistic) 0.734234

Reference No. 15 -- ADF Unit Root Test on BSE Sensex [trend & intercept]





Variable Coefficient Std. Error t-Statistic Prob. BSE(-1) -0.039094 0.035968 -1.086908 0.2797

C 82.13961 132.0319 0.622120 0.5353 @TREND(1996:06) 1.927460 1.083393 1.779097 0.0782

R-squared 0.031487 Mean dependent var 27.89750 Adjusted R-squared 0.012308 S.D. dependent var 300.1898 S.E. of regression 298.3367 Akaike info criterion 14.26274 Sum squared resid 8989485. Schwarz criterion 14.33903 Log likelihood -738.6627 F-statistic 1.641768 Durbin-Watson stat 1.945900 Prob(F-statistic) 0.198762

Reference No. 16-- ADF Unit Root Test on BSE Sensex [1st difference level]



Augmented Dickey-Fuller Test Equation Dependent Variable: D(BSE,2) Method: Least Squares Date: 06/11/06 Time: 21:16 Sample(adjusted): 1996:08 2005:02 Included observations: 103 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. D(BSE(-1)) -0.976913 0.098717 -9.896049 0.0000

R-squared 0.489775 Mean dependent var 4.208738 Adjusted R-squared 0.489775 S.D. dependent var 422.3126 S.E. of regression 301.6581 Akaike info criterion 14.26613 Sum squared resid 9281755. Schwarz criterion 14.29171 Log likelihood -733.7055 Durbin-Watson stat 2.005614

Reference no.17 Regression of X on Y and Y on X [NIFTY AND WPI]

Coefficients

UnstandardizedCoefficients

Standardized Coefficients

t Sig.

Model B Std. Error Beta 1 (Constant) 4.048 1.186 3.414 .001

WPI -3.027 1.181 -.230 -2.563 .012 Dependent Variable: NIFTY

Coefficients Unstandardized Coefficients

Standardized Coefficients

t Sig.

Model B Std. Error Beta 1 (Constant) 1.022 .007 148.650 .000

NIFTY -1.742E-02 .007 -.230 -2.563 .012 Dependent Variable: WPI

Reference no.18Residual based Co-integration Test on X on Y [Nifty & WPI]




Augmented Dickey-Fuller Test Equation Dependent Variable: D(E) Method: Least Squares Date: 06/11/06 Time: 21:48 Sample(adjusted): 1995:05 2005:03 Included observations: 119 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. E(-1) -1.105416 0.091474 -12.08444 0.0000

R-squared 0.553050 Mean dependent var -0.000955 Adjusted R-squared 0.553050 S.D. dependent var 0.105286 S.E. of regression 0.070388 Akaike info criterion -2.461212 Sum squared resid 0.584632 Schwarz criterion -2.437858 Log likelihood 147.4421 Durbin-Watson stat 1.940615

Reference no.19 Residual based Co-integration Test on Y on X

[WPI & Nifty]






Reference no.20Regression of X on Y and Y on X [BANK AND WPI]

Coefficients Unstandardized

CoefficientsStandardized

Coefficientst Sig.

Model B Std. Error

Beta

1 (Constant) 4.679 1.938 2.414 .019 WPI -3.643 1.930 -.235 -

1.888.064

Dependent Variable: BANK



Coefficientst Sig.

Model B Std. Error

Beta

1 (Constant) 1.020 .008 124.139

.000

BANK -1.515E-02 .008 -.235 -1.888 .064 Dependent Variable: WPI

Reference no.21 Residual based Co-integration Test on X on Y

[BANK & WPI]






Reference no.22 Residual based Co-integration Test on Y on X [WPI & Bank]







Reference no.23 Regression of X on Y and Y on X [BSE AND WPI]



Coefficientst Sig.

Model B Std. Error

Beta

1 (Constant) 4.166 1.312 3.174 .002 WPI -3.146 1.307 -.231 -2.407 .018

Dependent Variable: BSE



Coefficientst Sig.

Model B Std. Error

Beta

1 (Constant) 1.021 .007 143.720 .000 BSE -1.692E-02 .007 -.231 -2.407 .018

Dependent Variable: WPI

Reference no.24 Residual based Co-integration Test on X on Y [BSE & WPI]






R-squared 0.520206 Mean dependent var 3.79E-05 Adjusted R-squared 0.520206 S.D. dependent var 0.106673 S.E. of regression 0.073889 Akaike info criterion -2.362935 Sum squared resid 0.562338 Schwarz criterion -2.337508 Log likelihood 123.8726 Durbin-Watson stat 1.983658

Reference no.25 Residual based Co-integration Test on Y on X [BSE & WPI]







Reference no.26 Granger’s causality Test [NIFTY AND WPI]

F critical values:

No. of observations for different lags Level of significance 117

(lag 2) 115

(lag 4) 113

(lag 6) 107

(lag 12) 95

(lag 24) 1% 1.23 1.21 1.19 1.12 1.74 5% 1.32 1.29 1.27 1.20 1.07 10% 1.50 1.47 1.44 1.36 1.21

Reference no.27 Granger’s causality Test [BANK INDEX AND WPI]

F critical values


(lag 2) 58

(lag 4) 56

(lag 6) 50

(lag 12) (lag 24)

1% 1.40 1.35 1.31 1.17 -- 5% 1.53 1.48 1.43 1.28 -- 10% 1.84 1.78 1.72 1.53 --

Reference no.28 Granger’s causality Test [BSE SENSEX AND WPI]

F critical values


(lag 2) 101

(lag 4) 99

(lag 6) 93

(lag 12) 81

(lag 24) 1% 1.06 1.04 1.04 0.98 0.85 5% 1.15 1.13 1.11 0.05 0.91 10% 1.31 0.94 1.26 1.19 1.03

Relationship between Stock Index Returns and Inflation-Megha -0488

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